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Atoll 3 1.2_measurement_calibration
1.
Measurement Module Atoll 3.1.2 ©
Forsk 2012 Confidential – Do not share without prior permission Slide 1
2.
1. SPM Calibration
Concepts 2. Guidelines for CW Measurement Surveys 3. Working With CW Measurements 4. Automatic Calibration Method 5. Analysing the Calibrated Model 6. Calibration Process Summary Training Programme © Forsk 2012 Confidential – Do not share without prior permission Slide 2
3.
1. SPM Calibration
Concepts Purpose of Model Calibration Introduction to the Standard Propagation Model (SPM) Requirements Quality targets © Forsk 2012 Confidential – Do not share without prior permission Slide 3
4.
Purpose of Model
Calibration The propagation model is the basis of cell planning in mobile networks Reliability of cell planning is closely related to the propagation model accuracy A good model calibration is therefore required To obtain a propagation model consistent with the actual radio environment To improve the accuracy of coverage predictions To properly estimate interference The model calibration process entails three main procedures: Collecting CW (Continuous Wave) measurement data • Site location • Constructing test platform • Drive test Post-processing the CW measurement data • Data filtering Calibrating the model © Forsk 2012 Slide 4Confidential – Do not share without prior permission
5.
Introduction to the
Standard Propagation Model (SPM) Standard Propagation Model (SPM) Macrocell statistical propagation model Well suited for predictions in the 150 to 3500 MHz band Based on empirical formulas + set of parameters Numerous elements considered in propagation Frequency Distance between TX and RX Area type (urban, suburban, rural, etc.) Geography (relief, vegetation, climate, etc.) Effective height of TX/RX antennas Default values in new projects ! Calibration is essential to accurately estimate • Coverage predictions • Interference © Forsk 2012 Confidential – Do not share without prior permission Slide 5 LOShill,clutterRxeff7Rxeff6 Txeff54Txeff321model Kclutter.fKH.logKH.K Hlog.d.logKLossnDiffractio.KH.logKd.logKKL
6.
Requirements (1/2) Accurate and
recent geo data DTM and clutter resolution ≤ 25m for urban areas DTM and clutter resolution ≤ 50m for rural areas Vector map with main roads CW measurement surveys Site selection (for each area type – frequency band) • 8 recommended (6 minimum ) sites for calibration • 2 sites for verification Selection of different area types representative of the studied city • All main clutter classes should be represented CW surveys must be performed by stringently following guidelines © Forsk 2012 Confidential – Do not share without prior permission Slide 6
7.
Requirements (2/2) Drive Test
data Possible but not recommended ! Conversion to CW measurements is needed Downsides Real network is measured Interference Several frequencies are measured Directional antennas Accuracy of pattern (only a few points are relevant) Low sampling rate for each measured station (Lee criterion can’t be met) Signal measured over a short distance from the transmitter (model will not be calibrated for interference evaluation) It is not recommended to use Drive Test data to calibrate a propagation model ! © Forsk 2012 Confidential – Do not share without prior permission Slide 7
8.
Quality Targets Overall objective
: Minimize the error between the propagation model and the CW survey data Quality targets for calibration sites Global mean error on calibration sites < 1 dB Global standard deviation on calibration sites < 8 dB Mean error on each calibration site < 2.5 dB Standard deviation on each calibration site < 8.5 dB Quality targets for verification sites Global mean error on verification sites < 2 dB Global standard deviation on verification sites < 8.5 dB © Forsk 2012 Confidential – Do not share without prior permission Slide 8
9.
1. SPM Calibration
Concepts 2. Guidelines for CW Measurement Surveys 3. Working With CW Measurements 4. Automatic Calibration Method 5. Analysing the Calibrated Model 6. Calibration Process Summary Training Programme © Forsk 2012 Confidential – Do not share without prior permission Slide 9
10.
2. Guidelines for
CW Measurement Surveys Site Preselection criteria Survey route criteria Radio criteria © Forsk 2012 Confidential – Do not share without prior permission Slide 10
11.
Site Preselection Criteria Surrounding Very
representative of area type Major clutter classes equally represented No major obstruction within a radius of 150 to 200m from the CW sites Low diffraction within a 10km radius (rural zones) Enough roads all around the site Inspection on site Possibility to set up omnidirectional antenna • No obstacle on any side Panoramic photographs Report site details: precise height, coordinates ... © Forsk 2012 Confidential – Do not share without prior permission Slide 11
12.
Survey Route Criteria Distance Up
to noise floor of the receiver • Rural ± 10kms / Suburban ± 2kms / Urban ± 1km Equal number of samples near and far in all directions Clutter Routes through major clutter classes Avoid forests and lakes between transmitter and receiver Maps Supply vector maps of survey routes to import in Atoll Check that survey routes and roads (vector data or scanned maps) match ! © Forsk 2012 Confidential – Do not share without prior permission Slide 12
13.
Radio Criteria (1) Frequency 3
contiguous unused channels for GSM 1 unused carrier for UMTS Only one channel must be measured Interference must be checked before each drive Equipment data Antenna patterns + downtilt + azimuth (if not perfectly omnidirectional) Antenna height + transmit power + transmission gain (antenna) and losses (feeder) Receiver height + sensitivity + reception gain and losses © Forsk 2012 Confidential – Do not share without prior permission Slide 13
14.
Radio Criteria (2) Signal
measurement Lee criterion: at least 36 samples over 40λ (for f ≥ 900 MHz) • Maximum vehicle speed depends on equipment’s sampling rate Averaging samples over 40λ aims to remove fast fading effect ! Measurements after averaging At least 5000 points per site Typical number: between 10000 and 20000 points Sampling Rate at 900 MHz (samples per second) Sampling Rate at 2100 MHz (samples per second) Max. Speed (km/h) 45 100 60 68 150 90 90 200 120 113 250 150 © Forsk 2012 Confidential – Do not share without prior permission Slide 14
15.
1. SPM Calibration
Concepts 2. Guidelines for CW Measurement Surveys 3. Working With CW Measurements 4. Automatic Calibration Method 5. Analysing the Calibrated Model 6. Calibration Process Summary Training Programme © Forsk 2012 Confidential – Do not share without prior permission Slide 15
16.
3. Working with
CW Measurements Creating a CW measurement path By importing any ASCII format file • Standard import as in Excel • Option of importing any additional information related to CW measurement points • Definition and storage of import configurations By copying – pasting X,Y, measurement © Forsk 2012 Confidential – Do not share without prior permission Slide 16
17.
3. Working With
CW Measurements CW Measurements: Table List of all measurement points with their attributes and additional information Standard content management and tools (filters, copy-paste, etc...) Coordinates of points Signal Measured values Altitude, Clutter classes and heights, Distance, etc. read from the Geo data © Forsk 2012 Confidential – Do not share without prior permission Slide 17
18.
3. Working With
CW Measurements CW Measurements: Properties The points can be displayed according to any data contained in the measurement Table For predictions along the CW measurement path, you can either use Existing path loss matrices or recalculate them by choosing a specific Propagation model © Forsk 2012 Confidential – Do not share without prior permission Slide 18
19.
3. Working With
CW Measurements CW Measurements: Calculations and Statistics To calculate the predicted signal level of the reference (and any other optionally added) transmitter along the considered path. Note: This can also be run from top folders. To compare statistics between measured and predicted signal levels. Note: This can also be run from top folders. © Forsk 2012 Confidential – Do not share without prior permission Slide 19
20.
3. Working With
CW Measurements CW Measurements: Filter (at Folder level) Distance, Measurements values and Azimuth filtering Advanced filter on additional survey data Clutter Classes filtering © Forsk 2012 Confidential – Do not share without prior permission Slide 20
21.
3. Working With
CW Measurements CW Measurements: Filtering Assistant and Filtering Zones Tool to filter the data path in an more advanced way than in the Filter dialogue available at the folder level (previous slide) Tool to exclude some points from the measurement path according to a drawn polygon (all points within the polygon will be filtered out) © Forsk 2012 Confidential – Do not share without prior permission Slide 21
22.
3. Working With
CW Measurements CW Measurements: Smoothing Smoothing can be used to limit fading effect Smoothing keeps the number of measurement points unchanged Smoothing cannot be used to average gross CW measurements BEFORE AFTER Create a sliding window to smooth the measured signal strength © Forsk 2012 Confidential – Do not share without prior permission Slide 22
23.
3. Working With
CW Measurements CW Measurements: Synchronise the Table, the Map and the CW Measurements Tool Synchronisation: - Map - Table - CW Measurements Tool Analysis of a specific CW measurement path Measured signal level Predicted signal level Display of any attribute related to a given path © Forsk 2012 Confidential – Do not share without prior permission Slide 23
24.
1. SPM Calibration
Concepts 2. Guidelines for CW Measurement Surveys 3. Working With CW Measurements 4. Automatic Calibration Method 5. Analysing the Calibrated Model 6. Calibration Process Summary Training Programme © Forsk 2012 Confidential – Do not share without prior permission Slide 24
25.
4. Automatic Calibration
Method CW measurements pre-processing Calibration / verification stations Initial model Calibration wizard Final model © Forsk 2012 Confidential – Do not share without prior permission Slide 25
26.
CW Measurements Pre-processing Correspondence
between Measurements and Geo data Projection checking • Check that CW measurements and roads (from vector maps) match Routes checking • Check that CW measurements respect planned survey routes Surrounding checking • Check, with panoramic photographs, that there is no obstacle • Option of setting an angle filter to avoid attenuation due to obstacles © Forsk 2012 Confidential – Do not share without prior permission Slide 26
27.
CW Measurements Pre-processing Filtering Available
at the Folder level for each site Will be applied to all the measurement paths in that folder Distance, Measurements values and Azimuth filtering Advanced filter on additional survey data Clutter Classes filtering Will permanently remove the points outside the filter © Forsk 2012 Confidential – Do not share without prior permission Slide 27
28.
CW Measurements Pre-processing Distance
filtering (Min Distance / Max Distance) Typical min value: 200 m (not representative of mean propagation) Typical max value: 10 km (rural area) Signal filtering (Min Measurement / Max Measurement) Filtering out the measurements above the receiver overload: typical value -40 dBm Filtering out the measurements below the “receiver sensitivity + target standard deviation” typical value: -120 + 8 = -112 dBm • In order to avoid noise saturation effect in statistical results Azimuth filtering To remove points in a certain angle Filtering assistant In addition to the Filter located at the Folder level, you can define more precise filtering depending on the CW measurement file © Forsk 2012 Confidential – Do not share without prior permission Slide 28
29.
CW Measurements Pre-processing Filtering
assistant (1/2) Display of M = f ( 10log(D) ) Selection rectangle simultaneous Signal/Distance filtering Signal/Distance filtering according to the selection rectangle Selection RectangleAzimuth filtering on the measurement points Possibility to keep the selected points or to exclude them © Forsk 2012 Confidential – Do not share without prior permission Slide 29
30.
CW Measurements Pre-processing Filtering
assistant (2/2) Remaining points after the Distance, Signal level, Azimuth and Clutter classes filtering Remove all previous filters applied © Forsk 2012 Confidential – Do not share without prior permission Slide 30
31.
CW Measurements Pre-processing Final
filtering (1/2) Display each CW measurement according to their Measured signal level Check that propagation loss is spatially homogeneous © Forsk 2012 Confidential – Do not share without prior permission Slide 31
32.
CW Measurements Pre-processing Final
filtering (2/2) Removing which points? • Sudden drop of signal level • Suspicious areas ( Waveguide effect!) How? • Delete from the CW measurement table • Draw Filtering zones © Forsk 2012 Confidential – Do not share without prior permission Slide 32
33.
Calibration / Verification
Stations Calibration stations Stations so that measurements cover the whole area Avoid keeping stations with a lot of common points Verification stations Stations so that measurements are inside covered area (not at edges!) Major part of their covered areas are also covered by calibration stations How many ? If 7-8 measured stations: • 6 for calibration; 1-2 for verification If < 7 measured stations: • All stations used for calibration • Verification performed with same stations © Forsk 2012 Confidential – Do not share without prior permission Slide 33
34.
Initial Model General SPM
formula Ki values Let K6 = 0 Others will be calibrated Effective antenna height Choose method according to terrain relief Modify height from transmitter properties Can be selected by the calibration process Recommendation if terrain is hilly: “Enhanced slope at receiver” method Hilly terrain correction “1-YES” Recommendation if terrain is flat: “Height above average profile” method Hilly terrain correction “0-NO” LOShill,clutterRxeff7Rxeff6 Txeff54Txeff321model Kclutter.fKH.logKH.K Hlog.d.logKLossnDiffractio.KH.logKd.logKKL © Forsk 2012 Confidential – Do not share without prior permission Slide 34
35.
Initial Model Max distance Forced
to 0 during calibration If >0 no continuity ensured KClutter = 1 is recommended Multiplying factor of clutter losses Minimum loss = Free space loss Avoid unrealistic values Profiles Radial optimisation • Quicker © Forsk 2012 Confidential – Do not share without prior permission Slide 35
36.
Initial Model Heights of
Clutter taken or not into account in Diffraction: If you have a Clutter Heights file • Then put “1-YES” in the box If you only have a Clutter classes File • 2 approaches: • If Clutter Classes file has a very fine resolution • You can put “1-YES” and the tool will take into account the average heights defined in your clutter classes file • You should keep all the losses per clutter class to Zero • If Clutter Classes file resolution is low • Do not take into account the average heights defined in your clutter classes file (“0-NO”), but instead add a Loss per Clutter class type Receiver on top of clutter By default “No” Only useful for fixed receivers © Forsk 2012 Confidential – Do not share without prior permission Slide 36
37.
Initial Model Clutter Classes
Losses can be calibrated You need to define the Max distance from the Receiver (towards the Transmitter) for which the different clutter classes will be considered (typically 5X the clutter resolution) Choice between 4 types of Weighting functions (Uniform, Triangular, Logarithmic, Exponential) n i iiwLclutterf 1 wi=f(d'i) d'i wi Uniform Triangular Logarithmic Exponential © Forsk 2012 Confidential – Do not share without prior permission Slide 37
38.
Initial Model Reference model Create
a Reference model containing all the previous settings Duplicate this Reference model for each calibration, and give it a relevant name When duplicated, choose an appropriate name and pay specific attention to: • Methods used for Diffraction and Effective Antenna Height calculation • Value of Kclutter • Hilly terrain correction • Heights of Clutter considered or not in Diffraction • Clutter Range and associated Weighting function Start from the Reference model for each calibration trial © Forsk 2012 Confidential – Do not share without prior permission Slide 38
39.
Calibration Wizard Automatic calibration
overview Algorithm based on solving a least-squares problem Calculation of the best solution in terms of root mean square : Simple, fast and reproducible procedure First Step Selection of calibration stations Mδ 22 RMS © Forsk 2012 Confidential – Do not share without prior permission Slide 39
40.
Calibration Wizard Second step
(1/2) Selection of the Parameters to calibrate Possibility to modify their ranges © Forsk 2012 Confidential – Do not share without prior permission Slide 40
41.
Calibration Wizard Second step
(2/2) Recommended ranges It is recommended to leave K6 to 0 Constant Min Max K1 0 100 K2 20 70 K3 -20 20 K4 0 1 K5 -10 0 K7 -10 0 © Forsk 2012 Confidential – Do not share without prior permission Slide 41
42.
Calibration Wizard Final step Display
of “Before” and “After” parameters values and statistics (Mean error, Standard Deviation, RMS) Commit will update the model you are calibrating with the new values of Ki, height and diffraction methods as well as the Clutter Losses © Forsk 2012 Confidential – Do not share without prior permission Slide 42
43.
Final Model Extrapolate non-calibrated
clutter losses (1/2) Non-calibrated clutter classes must not have their clutter losses left to 0 • Could lead to high error where these classes are present Must be extrapolated from • Calibrated clutter losses (from other propagation model) • Typical losses (here centred on the Urban class) Clutter class Typical loss Dense Urban from 4 to 5 Woodland from 2 to 3 Urban 0 Suburban from -5 to -3 Industrial from -5 to -3 Open in urban from -6 to -4 Open from -12 to -10 Water from -14 to -12 © Forsk 2012 Confidential – Do not share without prior permission Slide 43
44.
Final Model Extrapolate non-calibrated
clutter losses (2/2) Centre clutter losses • Relative difference between clutters kept unchanged • Use K1 to balance Example: Apply scaling factor • Adapt typical losses (or calibrated ones coming from other model) to the calibrated model After calibration, model centred on suburban: K1=17.4 Losses: Dense Urban = 6.5 Wood = 5.7 Urban = 3.5 Suburban = 0 Typical Losses “MyModel” Losses Urban 0 0 calibrated 4.5 3 calibrated Dense UrbanOpen -12 -8 Extrapolated C e n t r e d Define Scaling Factor After centring, new values: K1=20.9 Losses: Dense Urban = 3 Wood = 2.2 Urban = 0 Suburban = -3.5 © Forsk 2012 Confidential – Do not share without prior permission Slide 44
45.
1. SPM Calibration
Concepts 2. Guidelines for CW Measurement Surveys 3. Working With CW Measurements 4. Automatic Calibration Method 5. Analysing the Calibrated Model 6. Calibration Process Summary Training Programme © Forsk 2012 Confidential – Do not share without prior permission Slide 45
46.
5. Analysing The
Calibrated Model Statistics (1/2) Apply the new calibrated propagation model to your CW sites © Forsk 2012 Confidential – Do not share without prior permission Slide 46
47.
5. Analysing The
Calibrated Model Statistics (2/2) Check the Quality Targets (Std Deviation and Mean Error values) on the Calibration and Verification sites Statistics available • Globally, • per Clutter class, • per Transmitter, and per Measurement path Possibility to run the Statistics on all the Measurement paths, or on specific ones © Forsk 2012 Confidential – Do not share without prior permission Slide 47
48.
5. Analysing The
Calibrated Model Correlation (to be checked on the Calibration sites) Through the Assisted Calibration Wizard Displays, for each parameters to be calibrated (K1, K2, K3, etc.), the correlation of the variables log(D), log(Heff), Diff, etc. with the global Error Check if the Correlation values are between -0,1 and +0,1 Commit will apply the Correction values to the corresponding Ki values Notes: This will not take into account the Ki Ranges The calibration wizard will attempt to bring the correlation as close to zero as possible. The results will be a correction value that will be added or subtracted to the initial Ki value in the model © Forsk 2012 Confidential – Do not share without prior permission Slide 48
49.
5. Analysing The
Calibrated Model Display Error Recalculate the Predicted signal values (P) according to the calibrated propagation model Display the Error (P – M) between the CW Measurements values (M) and the Predicted values (P) © Forsk 2012 Confidential – Do not share without prior permission Slide 49
50.
5. Analysing The
Calibrated Model Display CW Measurements & associated Signal Level study Use the same shading on both displays to be able to compare them For each site, one by one Check the global behaviour of calibrated model © Forsk 2012 Confidential – Do not share without prior permission Slide 50
51.
5. Analysing The
Calibrated Model CW measurement and Profile windows Analysis along the path © Forsk 2012 Confidential – Do not share without prior permission Slide 51
52.
1. SPM Calibration
Concepts 2. Guidelines for CW Measurement Surveys 3. Post-process the CW Measurements Data 4. Automatic Calibration Method 5. Analysing the Calibrated Model 6. Calibration Process Summary Training Programme © Forsk 2012 Confidential – Do not share without prior permission Slide 52
53.
Calibration Process Summary Before
starting... Check Geographical Database quality & accuracy (DTM, clutter, vectors...) Define environments (hilly, flat / urban, rural...) to specify the required number of propagation models to be calibrated Measurements preparation Sites selection Survey roads Fulfil radio criteria Make & Average measurements Create Transmitters used for measurements in the Atoll document With exact configuration (coordinates, antenna type & height, EIRP, losses) Analyse & Filter measurements ( Pre-processing) Keep representative points and remove suspicious ones Choice of calibration / verification sites © Forsk 2012 Confidential – Do not share without prior permission Slide 53
54.
Calibration Process Summary Run
the automatic calibration Display statistics and compare results with target values (Std deviation and Mean error) for calibration sites: Global and Individual checking for verification sites: Global checking Extrapolate non-calibrated clutter losses Analyse calibrated model Display statistics Check correlation Maps displaying Error(P-M), Measurements & Signal Level Study, etc. Apply the calibrated model Apply resulting standard deviation per clutter in the clutter class description Apply the calibrated model to network’s transmitters (Transmitter PropertiesPropagation tab) © Forsk 2012 Confidential – Do not share without prior permission Slide 54
55.
Thank you © Forsk
2012 Confidential – Do not share without prior permission Slide 55
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